A Quarter Century Of Women And Minorities In Engineering At Northwestern University
Bibliographic record
Abstract
This presentation is not that of a planned research study, but rather is a review of over twenty-five years of experience with women and minority students in engineering at Northwestern University offered in an anecdotal mode.This is admittedly a focused view and not necessarily one representative of circumstances in other institutions, but it has provided an opportunity to observe some general behavior and expectations on the part of students.Northwestern's experiences have been many during this period.In the seventies, we were involved with Inroads, in which an on campus summer resident program was held for minority high school students with the intent of orienting them to math, science, and engineering.In June 1975, the Big Ten plus schools joined to establish CIC-MPME, Midwest Program for Minorities in Engineering, a consortium funded by the Sloan Foundation to develop a variety of approaches to bring more minorities into engineering.Later, from 1983 to 1988, there was activity with and membership on the board of the Chicago Area Pre-College Engineering Program (CAPCEP), a program to develop curricula and instruction in the Chicago Public Elementary Schools.However, our attention in this particular discussion is given to following the continuous effort from at least 1970 to the present to increase the numbers of women and minority students entering and graduating in engineering at Northwestern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".